scholarly journals Condition Monitoring of Sensors in a NPP Using Optimized PCA

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Wei Li ◽  
Minjun Peng ◽  
Yongkuo Liu ◽  
Shouyu Cheng ◽  
Nan Jiang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Nuclear Power ◽  
Model Simulation ◽  
Principal Component ◽  
Fault Detection And Isolation ◽  
Pca Method ◽  
The Common ◽  
Simulation Results ◽  
Selection Stage

An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

A PCA Based Fault Detection and Isolation Method for Train Locating System

2010 ◽  
Vol 20-23 ◽  
pp. 688-693
Author(s):  
Jiang Liu ◽  
Bai Gen Cai ◽  
Tao Tang ◽  
Jian Wang
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Fault Detection And Isolation ◽  
Practical Application ◽  
Isolation Method ◽  
Efficiency Factors ◽  
Simulation Results ◽  
And Performance ◽  
Locating System ◽  
Fault Detectors

Fault tolerance is crucial to the operating safety and performance of train locating system. Based on the requirements of reliability and safety for train locating, the fault characteristics of location measuring sensors are analyzed. Based on the structure of the train locating system, the fault-tolerant design of the system is given with the location filtering module for case, in which six fault detectors are employed to determine the configuration of the module. Then a PCA based fault detection and isolation method is proposed with Hawkins T2 statistics and the corresponding control limit. By dynamically adjusting the efficiency factors, fault could be detected and isolated as prior defined isolating strategies, and then the fault tolerant performance will be guaranteed. Simulation results demonstrate the high fault tolerant ability of the proposed approach and certain practical application value.

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